Brute force prey metabarcoding to explore the diets of small invertebrates

Abstract Prey metabarcoding has become a popular tool in molecular ecology for resolving trophic interactions at high resolution, from various sample types and animals. To date, most predator–prey studies of small‐sized animals (<1 mm) have met the problem of overabundant predator DNA in dietary samples by adding blocking primers/peptide nucleic acids. These primers aim to limit the PCR amplification and detection of the predator DNA but may introduce bias to the prey composition identified by interacting with sequences that are similar to those of the predator. Here we demonstrate the use of an alternative method to explore the prey of small marine copepods using whole‐body DNA extracts and deep, brute force metabarcoding of an 18S rDNA fragment. After processing and curating raw data from two sequencing runs of varying depths (0.4 and 5.4 billion raw reads), we isolated 1.3 and 52.2 million prey reads, with average depths of ~15,900 and ~120,000 prey reads per copepod individual, respectively. While data from both sequencing runs were sufficient to distinguish dietary compositions from disparate seasons, locations, and copepod species, greater sequencing depth led to better separation of clusters. As computation and sequencing are becoming ever more powerful and affordable, we expect the brute force approach to become a general standard for prey metabarcoding, as it offers a simple and affordable solution to consumers that is impractical to dissect or unknown to science.

, but these approaches are highly impractical for a large portion of invertebrates whose small bodies complicate stomach content extraction and produce comparatively small fecalia.
Microscopy has helped identify small prey from small consumers, but is laborious and biased in favor of big, well-preserved prey (Berg, 1979), and demands morphological and taxonomic expertise (Pompanon et al., 2012).Conversely, experimental monitoring of communities over time can detect trophic interactions, and even allow quantifying ingestion rates, but struggles in reproducing the natural variability.The species assortment would typically be limited to prey expected in advance, or that co-occurred with the consumer if a natural sample was used as the starting point.Prey metabarcoding has become popular because it allows identification of diverse prey from complex and partly digested material, and does not require considerable a priori knowledge of prey, or taxonomical or morphological knowledge from the researcher (Casper et al., 2007).In broad strokes, metabarcoding includes extraction of DNA from dietary material-most often regurgitate, feces or stomach content, PCR amplification of target DNA (the marker gene or barcode), sequencing of PCR amplicons, and culminates with taxonomic identification by comparing obtained sequences to those in a reference database (Santoferrara, 2019).
Deciding on a dietary material is an important step that depends on the logistics and ethics of sampling, or the nature of the species being studied, such as its size or tendency for violence (Pompanon et al., 2012).Medium-sized crustaceans like northern shrimp (Pandalus borealis) may be suitable for excision of stomach content (Urban et al., 2022), but small invertebrates (<1 mm) are challenging to dissect, and may require whole body extraction (e.g., Novotny et al., 2021;Zamora-Terol et al., 2020).This comes at a cost, however, because the majority of DNA in the sample will naturally stem from the consumer itself (Piñol et al., 2014(Piñol et al., , 2015)).An overabundance of consumer DNA may also be a challenge when other materials (e.g., feces or gut content) are sampled (Kohn & Wayne, 1997), but its concentration becomes severely exalted in extracts of whole bodies (Piñol et al., 2014).Hence, the DNA of interest is in minority, while the unexciting consumer DNA will compose a competitive majority.

Conceptually different approaches have been developed to enable
prey studies from such "mixed" DNA samples, for instance reducing amplification of consumer DNA by using blocking primers.Nowadays most popular approach was spurred when Nielsen et al. (1991) researched synthetic analogs to DNA.A polymer with peptide instead of a sugar-phosphate backbone showed particular promise because it formed stable hybrid duplexes with DNA (Nielsen et al., 1991).So-called peptide nucleic acids (PNAs) had higher melting temperatures than DNA (Egholm et al., 1993), went unrecognized by DNA polymerases, and could not initiate amplification by PCR (Orum et al., 1993).PNAs could be introduced prior to PCR to hybridize irreversibly with a target sequence and thereby suppress its amplification (Orum et al., 1993).
Comparatively rare but interesting sequences would thus be allowed to replicate to detectable abundances (e.g., eukaryote parasites of blue crab, Troedsson et al., 2008).Other variants of blocking primers have also been put to the test, such as oligonucleotides modified with inhibitory C3 spacers (Deagle et al., 2009;Vestheim & Jarman, 2008).
Although blocking primers have enabled many prey studies, there are issues that warrant attention.Like universal PCR primers typically used in metabarcoding, blocking primers can introduce bias during amplification (Elbrecht & Leese, 2015;Leray & Knowlton, 2017;Piñol et al., 2015).With universal primers, primer-template mismatches and stochasticity may result in skewed relative abundances of species of interest (Sipos et al., 2007).Blocking primer bias also relates to primer-template mismatches, but it is rather the lack of them that leads to unreliable results.A blocking primer should have zero mismatches with the consumer, and as many as possible mismatches with prey to limit off-target interactions.Piñol et al. (2015) showed that a blocking primer with four and five mismatches to interesting prey decreased their relative abundances.Hence, PNAs and blocking oligonucleotides may introduce strong taxonomic biases during amplification (Piñol et al., 2015(Piñol et al., , 2019)).Furthermore, the production of specific and ultimately successful blocking primers remains an expensive chemical procedure and relies on both consumer and prey sequences before the design can begin.
We have explored a simple, cost-efficient, and versatile approach to explore eukaryote prey of small invertebrate consumers.By sequencing deep to offset the overabundance of consumer DNA, we show that one can obtain ample 18S rDNA prey reads from mixed whole-body copepod extracts, while at the same time avoiding the costs and laborious design of potentially biased blocking primers.Conceptually, we argue that the brute force method holds a lot of promise because current sequencing and computation enable the acquiring and processing of large amounts of data, and continuous development will only improve these capabilities (Lightbody et al., 2019).Through two sequencing runs (a pilot and a full-scale) using two commercially available NGS platforms (Illumina HiSeq4000 and the scalable Illumina NovaSeq6000), we tested the brute force approach for the first time with marine invertebrates.Specifically, we tested if the increased sequencing throughput of the Illumina NovaSeq6000 platform was useful for offsetting the expected overabundance, and hence for identification of prey interactions.We report on the effect of sequencing depth for resolving prey composition and discuss the advantages and caveats of the brute force methodology for prey studies of small consumers.

| Sample collection
Copepods were collected on four seasonal cruises from the central Barents Sea to the Arctic Nansen basin northeast of Svalbard, Norway (Table S2).Cruises occurred during Autumn (5-27 August  (76.00 N,31.22 E),northern shelf (79.72 N,34.32 E),.84 E, positions varied due to sea-ice drift, Figure S1).Small-sized mesozooplankton (<1 mm) were collected in vertical 64 μm Bongo-net hauls (to full depth or max.1000 m, ascent = 0.3 m s −1 , descent = 0.5 m s −1 , 60 cm mouth diameter).All large and/or gelatinous animals (1-10 cm) were removed, and the remaining suspension was sieved (64 μm) to discard seawater.Ice-cold ethanol (96%, −20°C) was then used to rinse retained mesozooplankton, before transfer into a sample bottle.The container was topped up with ice-cold ethanol and stored at −20°C.
Surface sterilization with bleaching was excluded since the minute body size (<1 mm) of the copepods analyzed herein raised concerns regarding how the treatment could penetrate and potentially alter the dietary signal.Also, existing literature, mostly based on arthropods, is in disagreement regarding the efficacy of bleaching, with one study indicating little effect on the overall dietary signal (Miller-ter Kuile et al., 2021).DNA extraction was performed per manufacturer's protocol ("Tissue Spin Protocol," E.Z.N.A® Tissue DNA kit, Omega Bio-Tek), with a lowered elution volume of 2 × 50 μL elution buffer.One negative without material was included with every round of extraction.
We verified amplification in all copepods and lack of amplification in negative DNA extracts by PCR amplification using universal eukaryotic primers.The success of the amplifications was inspected by 1% agarose gel electrophoresis, confirming singleband PCR products in all samples and no amplification in all extraction negatives.
The primers also contained a leading 5′-end variable number of degenerate nucleotides (N, 2 ≤ 4) to increase sequence variability and hence Illumina sequencing quality (Wangensteen et al., 2018).PCR amplification and all downstream processing from here and onwards were conducted twice.First as a pilot with a limited number of samples and negatives (N 1 = 79), and finally with a full set of samples and negatives (N 2 = 456).Samples were processed using the exact same protocol unless stated otherwise.
PCR amplicons were then pooled, purified with MinElute™ spincolumns (Qiagen, Hilden, Germany), and quantified using the broad-range dsDNA assay on a Qubit 4™ fluorometer (Invitrogen by ThermoFisher).Sequencing-ready libraries were prepared from purified pools in accordance with the NEXTflex™ PCR-Free DNA Sequencing Kit (Bioo Scientific, Austin, TX, USA).Here, DNA templates (3000 ng total for each library) were first purified by size (retaining fragments >150 bp) with magnetic Agencourt AMPure XP beads (Beckman Coulter Genomics, CA, USA), then adenylated, and ligated with Illumina-compatible adapters.We used several Illumina-compatible adapters (NEXTflex™ DNA Barcode Adapter; Bioo Scientific) to distinguish libraries of 96 samples.The libraries were quantified by qPCR using the NEBNext® Library Quant Kit for Illumina® (New England Biolabs, MA, USA) to verify successful preparation.For the pilot sequencing one library of 79 samples ( 75real and 4 extraction negatives), was sequenced using 150 Paired-End (PE) chemistry on a HiSeq 4000 platform (Novogene Co., Ltd.).For the full sequencing run, five libraries with a total of 456 samples (437 real and 19 extraction negatives), were sequenced using 150 PE chemistry on two lanes of a NovaSeq6000 platform (Novogene Co., Ltd.).

| Curation of prey
To obtain datasets with putative prey only, the assigned reads were subjected to a two-step curation process in R studio (v. 4.1.3).First, the reads were manually filtered based on PR2 assigned taxonomy using "tidyverse" functions (Wickham et al., 2019).All Maxillopoda (which include both currently accepted taxonomic groups Oligostraca and Multicrustacea) reads were discarded to remove consumer DNA.
We acknowledge that maxillopods may compose a food source for the species studied but the short read-length used to capture prey from partly digested materials did not allow for distinguishing DNA from maxillopod prey and consumer.Taxa known to interact with copepods (any Copepoda) in symbiosis (parasitism, commensalism, and mutualism) were recorded from current literature (Bass et al., 2021;Cleary et al., 2017;Cleary & Durbin, 2016;Zamora-Terol et al., 2021) and used to discard likely non-dietary interactions.By inspection, we discarded several zOTUs assigned to unlikely prey including seed plants, insects, and mammals.Putative contaminants from the marine environment were likewise discarded, notably large gelatinous organisms (Cnidaria, Ctenophora).We acknowledge that also these taxa may have a dietary origin, but we consider it more plausible that most of the sequences originated from the batch sample from which the copepods were picked.Gelatinous organisms are sticky and fragile and have been suspected of contaminating other studies of copepods using similar methodologies (Cleary et al., 2017).
Decontam was used to identify and discard remaining contaminants (Davis et al., 2018).We chose the prevalence method, which compares the prevalence (presence/absence-based frequency) of zOTUs in real samples (copepods) and extraction negatives to flag zOTUs that are likely contaminants.Relatively few contaminants (2 and 38 for pilot and full datasets, respectively) were identified and discarded at this stage.The remaining zOTUs were considered putative prey and were stored with metadata as phyloseq objects (McMurdie & Holmes, 2013).

| Sample metrics and analyses
Read metrics (counts and number of zOTUs) were acquired using different summarizing functions during bioinformatic processing (grep, obigrep, gawk), and selection functions (tidyverse, Wickham et al., 2019) during taxonomic filtration in R. To isolate the effect of differential sequencing, we generated an additional dataset (referred to as "pilot full") by sub-setting-from the full dataset-the same real samples that were sequenced in the pilot (n = 75).To test how well the different datasets represent prey diversity, we performed rarefaction analyses with the rarecurve function (step = 10) of vegan (Oksanen et al., 2019), and calculated the average number of reads required to discover new zOTUs (rareslope, sample = sample total reads).

| Testing the effect of sequencing depth on prey composition
Non-metric multidimensional scaling (NMDS) plots were used to visualize how depth of sequencing and data transformation may influence prey composition resolution.Multiple variants were made to explore beta-diversity using both occurrence and presence/ absence-based dissimilarity metrics.All ordinations were computed from sample-wise dissimilarities from compositional data at zOTUlevel.The relative abundances of each zOTU were computed per sample by dividing the number of reads of each zOTU by the total of sample prey reads.Relative abundances were then used to compute Bray-Curtis dissimilarities (Bray & Curtis, 1957).Since a presence/ absence-based metric is less sensitive to biases introduced during PCR (e.g., preferential amplification), we chose to also generate Jaccard dissimilarities (Jaccard, 1901) from a presence/absencetable wherein a 0.01% relative abundance threshold was enforced to denote presence or absence of zOTUs.We used Scree-plots to find the appropriate dimensions for a conservative acceptance threshold of ≤0.1 stress.Final NMDS plots were calculated iteratively (trymax = 100) with adequate dimensions (k = 4 or 5) using the metaMDS function of vegan (Oksanen et al., 2019).PERMANOVA (nperm = 10,000) analyses were used to test if three explanatory variables (season, station, and species) accounted for the observed variance in prey composition.We subsequently tested if the grouped samples had homogenous and comparable dispersions (p ≥ .05),or if compositional differences in prey could be due to heterogenous dispersion among groups (p < .05,Betadisper).

| Sequencing metrics
The pilot sequencing (N = 79 including sequencing blanks) led to a total of 412 million paired-end raw reads, which initial processing steps (pairing, filtering by length, quality and ambiguity, and demultiplexing) reduced to 342 million ("Pilot," Table 1).Removal of chimeric, erroneous, and rare sequences further reduced the dataset, and after taxonomic identification 284 mill.reads (69% of raw) distributed over 49,697 zOTUs were isolated.Most of the reads that were subsequently filtered out were assigned to the consumer taxon Maxillopoda (97.8% of assigned reads), whereas reads identified as contaminants or symbionts accounted for 1.6% and 0.2%, respectively.The final dataset of putative prey counted 1.2 million reads (0.4% of the assigned reads) in 1500 zOTUs.Distributed over 75 real samples, the pilot averaged 16,000 prey reads per copepod consumer.Prey zOTUs were further divided among 175 species identifiers from 155 genera.
The final sequencing, with an increased number of samples (N = 456 including sequencing blanks), yielded 5.4 billion pairedend raw reads ("Full", Table 1).Of these, approximately 4.3 billion reads (79% of raw) in 130,000 zOTUs were subsequently assigned to taxonomy.After discarding zOTUs assigned to Maxillopoda (97.5% of assigned reads), contaminants (1.1%), and symbionts (0.2%), the putative prey counted 52.2 million reads in 22,391 zOTUs.The full dataset zOTUs were further divided among 559 species from 476 genera.Putative prey reads corresponded to 1.2% of the assigned reads, or 1.0% of the raw reads, and a mean depth of ~120,000 prey reads per copepod consumer.Compared to dividends from relevant literature using dissection or blocking primers, the average prey reads per sample of both sequencing runs were more than two times greater (Table 2).

| Rarefaction
We used rarefaction to infer whether the sampling depths were sufficient to describe the full prey composition (i.e., zOTU richness).In theory, when rarefaction curves approximate a plateau, only a few novel zOTUs will be found with increased sequencing depth, thus indicating that the samples represent the full diversity of prey.The datasets all displayed plateau-like curves (Figure 1), but curves of from the full sequencing run (pilot full, full) were arguably less steep.

| Ordination and statistics
We prepared NMDS plots of all three datasets ("Pilot," "Pilot full," and "Full", see explanation below) with abundance and presence/ absence-based dissimilarity metrics to investigate the importance of depth and transformation for determining the composition of prey (Figure 2).Depending on the dataset being used, ordination required four or five dimensions to reach a conservative and low stress level of 0.1.The pilot prey reads required fewer dimensions (k = 4) than the full dataset and the dataset consisting of the full subset (k = 5).Regardless of dataset or metric, prey composition differed significantly between copepods from different seasons and stations (PERMANOVA, p < .001, Figure 2).The most visually distinct clusters were found when using the season sampled for profiling prey compositions, and samples acquired during the pilot ("Pilot," Figure 2a,b) TA B L E 1 Summary of read and zOTU abundances before and during bioinformatic processing (Step 1-4), according to sample type (real samples or extraction negatives), and according to taxonomic identity (consumer, symbiont, contamination, prey).Note: The total number of samples (N) was presented for both sequencing runs, and the number of extraction negatives and real samples are indicated in parentheses for the pilot (n p ) and for the full sequencing (n f ).Sample types and identified taxa are also presented with percentage-wise contributions to the total of assigned reads (percentage of assigned; POA) or to assigned reads from real samples (POA † ).
TA B L E 2 A summary of recent studies using prey metabarcoding to assess copepod trophic interactions. Ref.

Dietary source
Assigned 18S

Avg. prey reads per sample
Fragment (bp) Note: The list includes the consumers studied, the dietary source of DNA extractions, the total numbers of assigned 18S reads from all sources (e.g., consumer, prey, symbiont, and contaminant), and prey.
In addition, we report the average prey reads per sample (calculated from reported sample size and total prey reads), the 18S fragment, and its putative length (bp).The use of blocking primers is listed with concentrations of blocking primer per concentration of template DNA.Putative methodological biases are listed, and we outline the conceptual requirements and suitable consumers for each approach.Abbreviation: NA, not available.S3) indicated however that the clusters observed may be influenced by heterogenous dispersion (e.g., Figure 2f).The copepod species sampled was a less significant predictor of pilot and pilot full diets (Figure 2a,c) when using Bray-Curtis as dissimilarity metric (p = .003and p = .03,respectively), than with Jaccard.For both datasets with greater depth ("Pilot full" and "Full"), Jaccard dissimilarities led to visually greater separation of clusters.

| DISCUSS ION
The pragmatic approach applied here facilitated prey studies of small-sized copepods despite an overabundance of noninformative consumer DNA.By scaling up the sequencing to offset losses in consumer reads, we avoided using expensive and potentially biased blocking primers.While the pilot sequencing resulted in approximating the full diversity of our samples, deeper sequencing reduced the zOTU discovery rate from 10.3 in the pilot to 3.6 and 4.5 new prey zOTUs per 1000 reads in the full pilot and full datasets, respectively.
Our results from small-sized marine copepods, which are known to consume a wide diversity of prey, thus suggest that upscaled brute force sequencing is a suitable general method for determining the prey of small consumers, regardless of a priori access to the consumer sequence or possibility of dissection.

| Sequencing; how deep is deep enough?
The brute force concept was initially tested by Piñol et al. (2014)  was not reported.Their overall recovery was 61,000 prey reads, translating to 6% of all assigned reads.In our study, the prey recovery rates were much lower, with prey sequences accounting for only 0.44% and 1.22% of the assigned reads in the "pilot" and "full" sequencing runs, respectively.Thus, deeper sequencing was needed to acquire enough reads to cover the full diversity of prey species.
Nonetheless, if comparing prey read metrics with recent studies using PNA or other blocking primers (Table 2), it becomes clear that the applied brute force approach yielded a high absolute prey read output (1.2 and 52.2 mill.prey reads), regardless of high consumer losses.Reaching similar numbers with the use of blocking primers, a recent study of Baltic Sea copepods reported 1.2 million prey reads from an unknown number of samples (Zamora-Terol et al., 2020).
The use of a PNA blocker also did not eliminate the consumer DNA problem completely, given that the total read count before discarding Maxillopoda ASVs was 12.8 million reads.Hence, despite using blocking primers, 89% of the sequences were uninformative and discarded.Cleary et al. (2017) also acquired a large dataset with a total of 11.3 million reads from 80 samples of Calanus glacialis copepods.
Although a PNA probe also here was used to block amplification of Calanus spp., only a smaller portion of the dataset belonged to prey taxa (638,231 reads, 7975 prey reads per sample).Whether the discarded reads were assigned to contaminant, symbiont or consumer sources was not reported, but we find it likely that the majority was sourced from the consumer DNA.Likewise, a study of Pseudocalanus spp.copepods found 28,000 prey sequences in 46 samples (Cleary et al., 2016).PNA-PCR was used also here to block amplification of the consumer, but as the authors did not report the initial read counts, the success rate of blocking is not known.

| What influences prey sequence recovery?
There are many factors that can influence the recovery of prey sequences and thus the success of the brute force approach.The length of the target sequence is important, with small amplicons being favored due to a longer half-life and hence an increased detectability (Kamenova et al., 2018).Whether the extracts are based upon recently ingested or heavily digested materials (e.g., feces) will also have an impact on recovery (Kamenova et al., 2018).For small  animals.In fact, over 3.8 billion of the sequence reads belonged to the top three abundant zOTUs, recruiting 1.6, 1.4, and 0.8 billion reads, respectively.We are confident that these represent the three sampled copepod species based on the abundance distribution and BLASTn searches of the NCBI nucleotide collection database (nt).
The first zOTU dominated samples of Oithona similis, the second dominated samples from Microcalanus spp., and the third dominated samples from Microsetella norvegica.Other abundant maxillopod zOTUs with similar distribution patterns were also identified.These may be sequencing artifacts, individual or population level variants of the gene sequenced, or represent copepod prey.These zOTUs highlight a drawback to the approach, as the short 18S rRNA gene fragment (~100-110 bp) and PR2 database achieved high prey read numbers and good protist coverage, but sacrificed on taxonomic resolution of metazoans.Thus we were unable to confidently distinguish between copepod prey and consumers, and conservatively chose to discard all Maxillopoda sequence reads from our analyses.This could be problematic, given that many small invertebrates, also the copepods studied here-may feed on organisms with similar genetic signatures (e.g., copepods feeding on copepods).
could have omnivore tendencies (Fortier et al., 2001;Schnack-Schiel & Mizdalski, 1994).This limitation in the ability to identify feeding on closely related organisms can be mitigated somewhat by also sequencing the metazoan barcoding gene CO1 (cytochrome c oxidase subunit I), which provides better resolution of metazoan taxonomy.
It is however not just consumer DNA that is problematic as contaminants may further dilute samples, making finding enough prey data more difficult.In the datasets analyzed in this study, we observed more reads from the four extraction negatives sequenced for the pilot dataset (10.8 mill.reads) than in the combined 20 extraction negatives from the full dataset (9.3 mill.reads).This holds also at the level of real samples, given that the pilot dataset contains a higher contaminant percentage (1.6%)than the full dataset (1.1%).
If we somehow "worked cleaner" during the preparation of the second sequencing run is not known, but we hypothesize that increased contamination could explain some of the observed gaps in prey recovery (0.4% in pilot vs. 1.2% in full dataset), since also contaminant Other possible influences on prey sequence recovery could be technical-for instance that deeper sequencing leads to a greater number of rare prey zOTUs surpassing a set cutoff value (we denoised each sample individually with a four reads cutoff), or related to the biology of the consumer.In addition to sequencing depth, sampling at an additional location and two more seasons in March and April-May, may have increased the number of unique prey zOTUs identified in the full dataset.Several Arctic copepod species may for example enter diapause prior to the polar night.A dormant state ensues, where energy spent on motility, reproduction, and feeding is drastically lowered (Conover, 1988).Because internal wax ester storages are used for energy, feeding activities are no longer required.Consequently, some copepod species may be expected to contain little or no prey DNA during certain periods of the year.
The copepods studied here are not expected to enter diapause, but may to some extent reduce metabolic rates during winter (Conover & Huntley, 1991).

| Advantages of the brute force method
We argue that for metabarcoding of dietary samples, the brute force method is superior to blocking primers by being simpler, cheaper, faster, and offering a less biased result.It is simpler because, theoretically, the trophic interactions of any animal may be sampled and studied regardless of whether it is well known to science or just recently discovered.With a blocking-primer approach, the mandatory first step would be to have the targeted barcode of the consumer sequenced (unless it already exists).With sequence in hand, a site must be found that is unique and targets the consumer only.Site selection is difficult, however, because the blocking primer must be designed based on the sequences of both consumers (should match blocking primer perfectly) and putative prey (should be different enough to not bind the blocking primer).Ideally, one should also test the blocking primers against putative prey to make sure that the mismatches hinder hybridization.Services like TestProbe have been used to test blocking primers against broad databases like Silva (e.g., Ray et al., 2016), but it would be naive to expect that all putative prey sequences are available, especially for little-known organisms sampled from frontier environments.Exactly how many nucleotide mismatches should be demanded, and at what point the blocking primers start affecting the amplification of other relevant sequences remains unknown.Piñol et al. (2015) found that amplification of nontarget DNA was blocked even when the number of mismatches were 4 and 5 base pairs.Also, prey studies often utilize extra short sequences that sacrifice on taxonomic resolution to allow detection of prey from partly digested material.These factors make the blocking primer approach particularly problematic.Designing primers that solely block the consumer within a short stretch of DNA can be difficult, and there are no guarantees of finding out

a
Figure 2c,d) formed more divergent clusters.Ordination of the complete set of samples ("Full," Figure 2e,f) returned a pattern typical of a seasonal transition, with prey compositions from successive seasons overlapping, and samples from disparate seasons (e.g., August and April/May) forming separate clusters.Successive Betadisper tests (TableS3) indicated however that the clusters observed may to determine the prey of the Oedothorax fuscus spider.Using a single pooled sample of 109 individual extracts they acquired what they called "ample" prey reads.Most sequence reads originated from the consumer, although the percentage of O. fuscus reads to the total

F
I G U R E 1 Rarefaction curves of prey data acquired from (a) the pilot (n = 75), (b) the upscaled sequencing run but with the copepod samples assessed in the pilot only (pilot full, n = 75), and (c) the full upscaled sequencing (n = 437).Vertical red lines indicate averages of (a) 16,000, (b) 74,000, and (c) 119,000 prey reads per copepod.Slopes indicate the average number of prey zOTUs discovered with each new read sequenced.
sequences may compete for amplification and sequencing.A lot of non-prey sequences would inevitably lead to a lower output of prey, and the more contaminated samples are the greater the problem.Moreover, contaminant sequences may have greater potential to compete during sequencing due to the low overall DNA concentrations from extracting individuals of small animals.Lowered elution volume or pooling several individuals per extraction may help toward this end.Future dietary studies may arguably increase the yield of prey sequences by paying attention to lab routines and making them as clean as possible.Further improvements could be made by employing additional extraction negatives, or through external bleaching of animals.The latter would have to be carefully tested, since the fiddly process of washing small consumers makes it difficult to give equal exposure to each sample, and because it remains unknown whether bleach may penetrate the small bodies to affect the dietary DNA.

Table S4 ,
and prey compositions are presented in greater detail in an upcoming publication (S.Flo, C. Svensen, K. Praebel, B. A. Bluhm and A. Vader, unpublished data).